Intrinsic network architecture predicts the effects elicited by intracranial electrical stimulation of the human brain

Abstract

Intracranial electrical stimulation (iES) of the human brain has long been known to elicit a remarkable variety of perceptual, motor and cognitive effects, but the functional–anatomical basis of this heterogeneity remains poorly understood. We conducted a whole-brain mapping of iES-elicited effects, collecting first-person reports following iES at 1,537 cortical sites in 67 participants implanted with intracranial electrodes. We found that intrinsic network membership and the principal gradient of functional connectivity strongly predicted the type and frequency of iES-elicited effects in a given brain region. While iES in unimodal brain networks at the base of the cortical hierarchy elicited frequent and simple effects, effects became increasingly rare, heterogeneous and complex in heteromodal and transmodal networks higher in the hierarchy. Our study provides a comprehensive exploration of the relationship between the hierarchical organization of intrinsic functional networks and the causal modulation of human behaviour and experience with iES.

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Fig. 1: Experimental protocol: intracranial electrode implantation, iES functional mapping, and data coding and aggregation.
Fig. 2: Elicitation rate of iES varies markedly across intrinsic networks (seven-network parcellation).
Fig. 3: Elicitation rate of iES varies markedly across intrinsic networks (17-network parcellation).
Fig. 4: Network-specific elicitation patterns are present at the level of individual patients.
Fig. 5: Relationships between network elicitation rates, position in the principal gradient hierarchy and the diversity of elicited effects.
Fig. 6: Representative patient reports following iES throughout the brain.

Data availability

The data that support the findings of this study are available from the corresponding authors upon request.

Code availability

Custom code that supports the findings of this study is available from the corresponding authors upon request.

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Acknowledgements

The authors are grateful to the many patients who participated, without whom this research would have been impossible, as well as numerous funding agencies for generous support. K.C.R.F. was supported by a Postdoctoral Fellowship from the Natural Sciences and Engineering Research Council (NSERC) of Canada, and is currently supported by a Medical Scholars Research Fellowship from the Stanford University School of Medicine. L.S. is supported by the China Scholarship Council (201708110057) and National Natural Science Foundation of China (81701268). B.L.F. is supported by the National Institutes of Health (R00MH103479). A.K. was supported by a Banting Postdoctoral Fellowship from the Canadian Institutes of Health Research (CIHR). J.P. is supported by the National Institutes of Health (1P50MH109429). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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K.C.R.F., O.R. and J.P. conceived of the study. K.C.R.F., L.S., D.S.M., A.K. and J.P. designed the study. K.C.R.F. and J.P. collected the data. K.C.R.F., L.S., S.B., O.R., B.L.F., S.S., D.S.M. and A.K. analysed the data. K.C.R.F., L.S., S.B. and O.R. created the figures. K.C.R.F. and J.P. wrote the initial draft of the manuscript. All authors participated in writing the final draft.

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Correspondence to Kieran C. R. Fox or Josef Parvizi.

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Extended data

Extended Data Fig. 1 Hemispheric asymmetries in elicitation rate.

For post-hoc individual network comparisons, statistical significance was set at p < .007 (α = .05 Bonferroni-corrected for seven multiple comparisons).

Extended Data Fig. 2 Reliability analyses for 7-network elicitation rates.

Seven-network parcellation: correlations between elicitation rate and principal gradient value across all reliability samples (Pearson’s r, [95% CIs]).

Extended Data Fig. 3 Reliability analyses for 17-network elicitation rates.

17-network parcellation: correlations between elicitation rate and principal gradient value across all reliability samples (Pearson’s r, [95% CIs]).

Extended Data Fig. 4 Effect categories elicited in the 7-network parcellation.

Frequency of effect types within each network.

Extended Data Fig. 5 Effect categories elicited in the 17-network parcellation.

Frequency of effect types within each network.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2 and Supplementary Tables 1–4.

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Fox, K.C.R., Shi, L., Baek, S. et al. Intrinsic network architecture predicts the effects elicited by intracranial electrical stimulation of the human brain. Nat Hum Behav (2020). https://doi.org/10.1038/s41562-020-0910-1

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